We will show later in the section  that NLPR outperforms state-of-the-art fusion techniques and is better able to capture texture patterns. In fact, NLPR is generally more powerful than local pixel regularizers and the improvement is visually apparent for applications such as inpainting and denoising. Two such results are shown in figures REF  and REF .
For denoising, we have used a RGB representation of the noisy multiband image as a guide. Since a reliable guide image is not available for inpainting, we have used a grayscale representation of the original image as a guide. We remark that such a guide is not available in practice for inpainting; the idea is simply to show how the restoration quality can be enhanced by using guide-based weighting. This is particularly apparent from the results in figure REF . We have also compared with local pixel-based VTV [1]}. Notice that the restored texture is visibly better for guided NLPR compared to VTV and unguided NLPR. For the denoising result in figure REF , notice that tiny details are smoothed out by VTV and BM4D [2]} but NLPR is able to retain fine structures.
